class_docstr: Critic network. The network follows the standard actor-critic architecture used in Deep Reinforcement Learning.
  The model is used in Counterfactual with Reinforcement Learning (CFRL) for both data modalities (images and
  tabular). The hidden dimension used for the all experiments is 256, which is a common choice in most benchmarks.
docstr: Constructor.
  
  Parameters
  ----------
  hidden_dim
      Hidden dimension.
docstr_tok: ["Constructor",".","Parameters","--","--","--","--","--","hidden_dim","Hidden","dimension","."]
code: def __init__(self, hidden_dim: int):
      """
      Constructor.
  
      Parameters
      ----------
      hidden_dim
          Hidden dimension.
      """
      super().__init__()
      self.fc1 = nn.LazyLinear(hidden_dim)
      self.ln1 = nn.LayerNorm(hidden_dim)
      self.fc2 = nn.LazyLinear(hidden_dim)
      self.ln2 = nn.LayerNorm(hidden_dim)
      self.fc3 = nn.LazyLinear(1)
  
file_path: alibi/alibi/models/pytorch/actor_critic.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Critic object from inherited base classes: nn with example: obj = Critic(hidden_dim)